MetaMeta: integrating metagenome analysis tools to improve taxonomic profiling

Abstract

BackgroundMany metagenome analysis tools are presently available to classify sequences and profile environmental samples. In particular, taxonomic profiling and binning methods are commonly used for such tasks. Tools available among these two categories make use of several techniques, e.g., read mapping, k-mer alignment, and composition analysis. Variations on the construction of the corresponding reference sequence databases are also common. In addition, different tools provide good results in different datasets and configurations. All this variation creates a complicated scenario to researchers to decide which methods to use. Installation, configuration and execution can also be difficult especially when dealing with multiple datasets and tools.ResultsWe propose MetaMeta: a pipeline to execute and integrate results from metagenome analysis tools. MetaMeta provides an easy workflow to run multiple tools with multiple samples, producing a single enhanced output profile for each sample. MetaMeta includes a database generation, pre-processing, execution, and integration steps, allowing easy execution and parallelization. The integration relies on the co-occurrence of organisms from different methods as the main feature to improve community profiling while accounting for differences in their databases.ConclusionsIn a controlled case with simulated and real data, we show that the integrated profiles of MetaMeta overcome the best single profile. Using the same input data, it provides more sensitive and reliable results with the presence of each organism being supported by several methods. MetaMeta uses Snakemake and has six pre-configured tools, all available at BioConda channel for easy installation (conda install -c bioconda metameta). The MetaMeta pipeline is open-source and can be downloaded at: https://gitlab.com/rki_bioinformatics.Electronic supplementary materialThe online version of this article (doi:10.1186/s40168-017-0318-y) contains supplementary material, which is available to authorized users.

Enhancing the Resolution of Rumen Microbial Classification from Metatranscriptomic Data Using Kraken and Mothur

“…The success of microbiome studies (composition, structure, diversity, and function) is primarily ascribable to the development of bioinformatics tools embedded in creative algorithms specially tailored to overcome the technical challenges posed by the analysis of massively paralleled, high-throughput sequencing data ( Simon and Daniel, 2011 ; Siegwald et al, 2017 ). These bioinformatics tools make use of several techniques (e.g., read mapping, k-mer alignment, and composition analysis) ( Piro et al, 2017 ) and can be categorized into two distinct groups: (1) programs that use all available genome sequences ( Lindgreen et al, 2016 ), also called assignment-first approaches ( Siegwald et al, 2017 ) (e.g., CLARK – Ounit et al, 2015 ; GOTTCHA – Freitas et al, 2015 ; KRAKEN – Wood and Salzberg, 2014 ; MG-RAST – Meyer et al, 2008 ), and (2) programs that target a set of marker genes ( Lindgreen et al, 2016 ), also known as clustering-first approaches ( Siegwald et al, 2017 ) (e.g., QIIME – Caporaso et al, 2010 ; MOTHUR – Schloss et al, 2009 ; MetaPhlAn – Segata et al, 2012 ; mOTU – Sunagawa et al, 2013 ). In the assignment-first tools, all reads are assigned to the lowest taxonomy unit (lower common ancestor-LCA) within a reference database based on their annotations, while in the clustering-first approaches the reads are grouped into Operational Taxonomic Units (OTUs) using different OTU picking strategies (closed or open reference) to assign reads to a taxonomic group based on their sequence similarities ( Siegwald et al, 2017 ).…”

Mesophilic Sporeformers Identified in Whey Powder by Using Shotgun Metagenomic Sequencing

“…This, coupled with the previously reported possibility of high levels of false positives resulting from Kaiju assignment (27) and the fact that MetaPhlAn2 works off only a subset of marker genes per species (28), is why Kraken was preferentially employed, with a filter threshold of 0.2 to increase precision without detrimentally impacting sensitivity. Furthermore, to reduce the possibility of false positives (27), taxa were included only if present at a minimum of 1% relative abundance in at least one sample; otherwise, reads were assigned as "others." B. cereus was found to be the dominant species in 7 of the 12 monthly mesophilic sporeformer-enriched samples, i.e., those from January, February, March, May, July, October, and November.…”

“…Disadvantages that need to be overcome in order to allow for the routine use of the sequencing technologies employed in this study primarily relate to the cost of analysis, which is currently too expensive for large-scale routine use. Additionally, there are challenges relating to assembly of genomes from shotgun metagenomic sequencing (22) and difficulties arising from insufficient accuracies associated, to different extents, with taxonomic classifiers (27). There are some solutions emerging, whereby new lower-cost, rapid sequencers are arriving on the market, with MinION (45) leading the way toward quick portable detection systems for microorganisms.…”

gNOMO: a multi-omics pipeline for integrated host and microbiome analysis of non-model organisms

“…Recently, research interests in microbial communities have been strongly increased due to findings on the impact of the microbiome on human health [11,12]. Microbiome studies often employ metaomics techniques such as metagenomics [13] that aims to analyze the genetic material from all members in a microbial community sample. Despite many advantages, metagenomics still presents a static gene-centric approach that cannot assess temporal dynamics and functional activities of complex microbial populations [14].…”

“…Used separately, metagenomics, metatranscriptomics, and metaproteomics are already powerful because they complement and mutually support each other. In the past, powerful tailored bioinformatic solutions have been developed for the individual meta-omics analysis levels [13,15,16]. However, the true strength unfolds when these analysis techniques are integrated [17,18].…”